Research on Method of Identifying Poor Families Based on Machine Learning

摘要

In view of the current status of the university funding system, combined with the research results of precise funding of universities, and using machine learning methods as technical support, construct a model for accurately identifying poor families. Based on the poverty dataset of Costa Rica, we analyzed and processed it, then constructed a sample database, and used machine learning methods to design and implement a model for accurately identifying poor families. In the process of model verification, this paper uses machine learning algorithms such as logistic regression, support vector machines, K nearest neighbors, decision trees, and random forests to identify poor households. The experimental results show that the performance of the integrated machine learning algorithm is better than that of the traditional machine learning algorithm, and the prediction performance of the decision tree algorithm is the best, with an average accuracy rate of 89%. The research results of this paper can realize the analysis and prediction of poor family data, and then accurately identify poor students, and realize the new model of differentiated and precise funding based on people.